No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by WanLanglin · Agent Tool · ★ 95
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is -awesome-cc-harness safe to install? View the security audit →
awesome-cc-harness English (TL;DR) Reverse-engineering all 512,664 lines of Claude Code's TypeScript source — agent loop, tool system, permission model, sandbox, context engineering. A 16-chapter textbook (50,000 words, 147 code blocks, 77 diagrams) on how Anthropic actually builds an agent harness. 📣 Featured in Chinese AI media — Republished by QingkeAI (青稞AI) and other WeChat publications, with 20,000+ reads and 2,000+ shares. Original Chinese article. Two findings you won't see elsewhere: 🔬 Grove — Anthropic's hidden training-data pipeline. Retention jumps 30 days → 5 years when enabled. 796 telemetry events flow into BigQuery columns the source code labels "training data". 🛡️ [Anti-Distillation](https://wanlanglin.g
| Stars | 95 |
| Forks | 17 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 59.7536044841206/100 |
| Last Updated | 2026-05-01 |
| Created | 2026-03-31 |
| Platforms | claude-code, python |
| Est. Tokens | ~515k |
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-awesome-cc-harness is Reverse-engineering Claude Code's 512K LOC TypeScript source: agent loop, tool system, permission model, Grove training pipeline, anti-distillation defense. It is categorized as a Agent Tool with 95 GitHub stars.
-awesome-cc-harness is primarily written in Python. It covers topics such as agent-harness, agent-loop, ai-agents.
You can find installation instructions and usage details in the -awesome-cc-harness GitHub repository at github.com/WanLanglin/-awesome-cc-harness. The project has 95 stars and 17 forks, indicating an active community.
The top alternatives to -awesome-cc-harness on Agent Skills Hub include athena-loops, claudepro-directory, garcon. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: